Algorithms for clustering data
Algorithms for clustering data
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
An interactive visual query environment for exploring data
Proceedings of the 10th annual ACM symposium on User interface software and technology
An introduction to natural computation
An introduction to natural computation
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Exploiting Hierarchy in Text Categorization
Information Retrieval
The Vision of Autonomic Computing
Computer
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
Mining Temporal Patterns Without Predefined Time Windows
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining Logs Files for Computing System Management
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Towards informatic analysis of syslogs
CLUSTER '04 Proceedings of the 2004 IEEE International Conference on Cluster Computing
Discovering actionable patterns in event data
IBM Systems Journal
Tracking in a spaghetti bowl: monitoring transactions using footprints
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Tracking transaction footprints for non-intrusive end-to-end monitoring
Cluster Computing
A universal method for composing business transaction models using logs
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Event log mining tool for large scale HPC systems
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Towards implicit knowledge discovery from ontology change log data
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
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With advancement in science and technology, computing systems are becoming increasingly more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming increasingly more difficult to monitor, manage and maintain. Traditional approaches to system management have been largely based on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. This has been well known and experienced as a cumber-some, labor intensive, and error prone process. In addition, this process is difficult to keep up with the rapidly changing environments. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing systems.A popular approach to system management is based on analyzing system log files. However, some new aspects of the log files have been less emphasized in existing methods from data mining and machine learning community. The various formats and relatively short text messages of log files, and temporal characteristics in data representation pose new challenges. In this paper, we will describe our research efforts on mining system log files for automatic management. In particular, we apply text mining techniques to categorize messages in log files into common situations, improve categorization accuracy by considering the temporal characteristics of log messages, and utilize visualization tools to evaluate and validate the interesting temporal patterns for system management.